A Workflow for Testing Price Series and Building Multi-Timeframe Features

Summary

This analysis workflow loads historical bars into a time-indexed table and plots closing prices to inspect gaps. It applies a Ljung–Box test for randomness, an Augmented Dickey–Fuller test for stationarity, and autocorrelation plots to examine serial dependence. It then examines percentage price changes and relative volatility, defining the latter as ATR minus a fixed cost proportional to closing price, and reports distribution statistics such as mean, median, skew, and kurtosis.

The workflow can resample bars into larger intervals and calculate selected technical indicators for each resulting series. It also plots threshold crossings around a moving average and standard-deviation bands as candidate signals. No empirical conclusions or trading performance are provided. The diagnostics are exploratory, and their interpretation depends on the series, test assumptions, parameter choices, and correct implementation; the example does not establish that the plotted crossings are profitable.

Key ideas

• The workflow checks historical price data visually and with randomness, stationarity, and autocorrelation diagnostics.
• It summarizes percentage changes and ATR-based volatility relative to a proportional cost estimate.
• It supports resampling bars into multiple timeframes and calculating selected technical indicators.
• Moving-average and standard-deviation bands are used to visualize potential threshold-crossing signals.
• The document reports no tested strategy performance, so the plotted signals remain exploratory.

Tags

From a private course collection; the original is not published.